ARX Filtering of Single-Sweep Movement-Related Brain Macropotentials in Mono- and Multi-Channel Recordings

Abstract:

A technique of stochastic parametric identification and filtering is applied to the analysis of single-sweep event-related potentials. This procedure, called AutoRegressive with n exogenous inputs (ARXn), models the recorded signal as the sum of n+1 signals: the background EEG activity, modeled as an autoregressive process driven by white noise, and n signals, one of which represents a filtered version of a reference signal carrying the average information contained in each sweep. The other (n-1) signals could represent various sources of noise (i.e., artifacts, EOG, etc.). An evaluation of the effects of both artifact suppression and accurate selection of the average signal on mono- or multi-channel scalp recordings is presented.

Keywords

Event-Related Potentials - Single-Sweep Analysis - Movement-Related Brain Macropotentials

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